A Pixel Dichotomy Coupled Linear Kernel-Driven Model for Estimating Fractional Vegetation Cover in Arid Areas From High-Spatial-Resolution Images
Xu Ma, Jianli Ding, Tiejun Wang, Lei Lu, Hui Sun, Zhang Fei, Xiao Cheng, Ilyas Nurmemet
Abstract
With the increased use of high-spatial-resolution (HSR) images for vegetation monitoring in arid areas, more details of the low vegetation coverage and interference from the land “background” are captured in the corresponding images. From computational time and accuracy, the multi-angle method (MAM) in the pixel dichotomy model is a potential algorithm to apply in arid areas, but MAM needs the multi-angle vegetation index (VI) as the driver parameters. However, most HSR images are obtained in nadir mode, and the multi-angle information of reflectance is difficult to obtain, which limits the estimation of multi-angle VI from HSR images. To address this issue, this study used a “graphical method” to modify the radiation influence caused by the canopy structure and land “background.” We developed an inversion method of the linear kernel-driven model (KDM) and designed a random sampling method to estimate multi-angle VI from HSR images. Then, we proposed a new pixel dichotomy coupled linear KDM (PDKDM), validated using simulated, field-measured, and reference data. The results showed that the FVC in arid areas estimated by PDKDM was highly consistent with “true” data, with root-mean-square error (RMSE) < 0.062, RMSE < 1.125, and RMSE < 0.027 for comparison with simulated, field-measured and reference data, respectively. PDKDM addressed the issue with the previous MAMs to estimate FVC from HSR images in arid areas. This study provides a useful algorithm with high computational efficiency for producing HSR FVCs in arid areas.